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This invention is related to image processing and pattern recognition and more particularly to automatically generating templates and searching for alignment in multi-resolution images using those templates.
Many industrial applications such as electronic assembly and semiconductor manufacturing processes require automatic alignment. The alignment can be performed using pre-defined fiducial marks. This requires that marks be added to the subjects. This process limits the flexibility of the alignment options, increases system complexity, and may require complex standardization or, at a minimum, prior coordination. It is desirable to use a portion of the design structures of the subject as templates for the alignment purpose without adding specific fiducial marks. This removes the extra steps required to produce and insert the special fiducial marks.
The images of design structures of a subject such as circuit board or a region of a wafer can be acquired for alignment processing. However, the acquired images often exhibit low contrast and may be blurry or noisy in practical applications due to process characteristics and non-uniform illumination and noisy imaging system due to cost constraint. Therefore, both the template generation and the template searching processes could be challenging.
The automatically generated templates must be xe2x80x9cstablexe2x80x9d so that the search algorithm rarely misses the correct template location even if the contrast of the image varies. This is challenging since the images for template generation could include any customer designed patterns. Furthermore, image variations such as image contrast variations, image noise, defocusing, image rotation error and significant image shift greatly reduce the stability of image features.
Search for and estimation of template location technology can be applied to object tracking or alignment. A tracking system often requires location estimate of moving objects of interest. In an alignment application, the template search result is often used to dynamically adjust the position and orientation of the subjects. In both cases, a fast search and estimation method is required. This is challenging, especially for a large image.
A good template should have unique structures to assure that it will not be confused with other structures. It also needs to have stable and easily detectable features to ease the template searching process. In the current practice, a human operator selects the template region using his judgment and experience and a template matching process (usually normalized correlation) is used to search for the selected template. Unfortunately, it is difficult for a human operator to judge the goodness of design structure for template search in the template generation process. Therefore, template search accuracy and repeatability could be compromised in a low contrast and noisy situation. This demands an automatic method and process for the generation of a template from the design structures of a subject.
Prior art uses simple template matching. This method needs intense calculation and as a result the searching speed is slow. Another problem is that the template generation is a manual process requiring training and experience. This can lead to poor or variable performance when using the template for alignment because of the poor template generation. Furthermore, the template pattern is simply a sub-region of the image. There is no image enhancement or multi-scale feature extraction. This significantly limits the robustness and speed of the prior art approach.
It is an object of this invention to automatically select a template or system of templates for alignment use. Using this template, no (or less) special fiducial marking is required.
It is an object of the invention to teach methods for signal enhancement for template generation.
It is an object of the invention to teach discrimination methods for template generation.
It is an object of the invention to teach learning methods for compensating for image variation and noise associated with a particular template search application and thereby reduces the deleterious effects of such variability.
It is an object of this invention to use a multi-resolution image representation of a subject to speed alignment processing and to increase robustness.
It is an object of this invention to teach use of coarse to fine processing using multi-resolution images to direct the template search and to increase its speed.
It is an object of this invention to develop image pre-processing that improves template search robustness and accuracy.
It is an object of this invention to provide separate image pre-processing and separate template generation for each resolution level of the multi-resolution image.
It is an object of this invention to allow the software implementation of the fast search method in a general computer platform without any special hardware to reduce cost and system complexity.
Alignment of industrial processes is commonly done using fiducial marks, marks that are added for the alignment or registration purpose. It is also possible to use portions of the images (i.e. a template) of the processed materials themselves to provide the reference needed for alignment. Selection of the template region is important to the robustness and accuracy of the resulting alignment process. In the invention, methods for automatic selection of the template region are taught. In addition, the invention improves overall signal to noise for template search (1) by use of structure specific image pre-processing, (2) a consistent template selection method based (in one embodiment) on an exhaustive search of all possible locations and pre-processing alternatives, (3) use of learning to reduce application specific variability, (4) use of multi-resolution image representation to speed template searching and template generation, (5) use of a coarse resolution to fine resolution search process, and (6) specific resolution level selection of template location, and (7) a discriminate function to guide automatic generation of templates and image pre-processing method. Matching methods for robustly locating the template within selected search possibilities are also taught.